Not Just Another Saturday Night: Strategizing Safety with Non-Urban Women Experiencing Intimate Partner Violence in Ontario
Bibliographic record
Abstract
Women living in non-urban communities are a high-risk population for intimate partner homicide (Dawson et al., 2019; Northcott, 2011). However, studies that have examined strategies women employed to respond to intimate partner violence (IPV) and safety planning are limited and urban-centric (Doherty, 2017). This dissertation answers four research questions related to increasing safety for women experiencing IPV in non-urban communities: (1) How do women living in non-urban communities experiencing IPV conceptualize safety? (2) How do women living in non-urban communities protect themselves from violence perpetrated by an intimate partner? (3) What challenges do service providers encounter when developing safety plans with women living in non-urban communities experiencing IPV?; and (4) How do women and service providers’ perspectives align on how to increase safety in non-urban communities? This dissertation embraced methodological pluralism, drawing from social ecology, intersectionality and emotional geography to investigate women’s perceptions of fear and safety, strategies women employed to be safer and challenges service providers encountered developing safety plans. In-depth interviews with 20 women who were living in a non-urban community and experienced IPV revealed that safety is a holistic and multidimensional concept, encompassing spatial, mental, physical, social, and cultural dimensions, which addresses the first research question. To answer the second research question, women primarily deflected physical danger and relied on private strategies (i.e., placating, resistance) to protect themselves and their child(ren). Women’s responses were influenced by the geographic environment and their social positioning. Based on eight in-depth interviews with service providers, the inadequate police response and meeting women’s basic needs (e.g., housing, transportation, groceries) were among the greatest challenges when safety planning, which addresses the third research question. To answer the fourth research question, a rural-specific poverty strategy and increased funding for social, emergency, and transportation services are needed to protect women from abusive men in non-urban communities. Overall, this dissertation demonstrates that place matters (Pruitt, 2008) for understanding women’s experiences of IPV and increasing their safety. Therefore, policies and initiatives aimed at reducing gender-based violence in Canada must consider the geographic context in which women are living to overcome postcode (in)justice (George & Harris, 2014).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".